Abstract:
The research field of computer vision has recently taken interest in the active problem of masked
face recognition due to the COVID 19 pandemic. The use of face masks as a preventative measure
against the spread of COVID-19 has presented a new challenge for the technology of face
recognition. Masked Face Recognition (MFR) has emerged as a crucial issue within the field of
face recognition after the COVID-19 epidemic. MFR is a specific type of facial occlusion issue
that obstructs vital facial features such as the mouth, nose, or chin. The purpose of research on
Masked Face Detection and Recognition is to fine-tune a pre-trained model that can more
accurately recognize masked faces and detect whether the person is wearing a mask or not, which
can be advantageous in various applications such as security and surveillance, healthcare, retail,
law enforcement, the workplace, and social media. The objective of this dissertation is to examine
the potential of machine learning techniques for enhancing the performance of masked face
detection and recognition systems. This thesis proposes an approach to enhance the performance
of the single neural network architecture such as pretrained InceptionV3 as unified model capable
of both detection and recognition of masked images by achieving 99% and 98% respectively on
MFR2 dataset. Pretrained VGG16 with transfer learning and fine tuning is trained and tested on
publicly available datasets for the detection of masked faces, which are MDMFR dataset, Kaggle
face mask detection dataset, facedatahybrid and for recognition results obtained on MFR2. The
findings of this research offer valuable insights into the potential of pretrained networks with
transfer learning to improve the performance of masked face detection and recognition systems
and pave the way for future research in this area.
